Question 228045 in Mysql Question posted in
The official MySQL documentation can be found here.

Understanding Index creation in MySQL – Timestamp field

I want to understand how index is created in MySQL – meaning if its a TimeStamp field – will it create a hash of the TimeStamp field and store it or just store the literal Value of the timestamp. Since timestamp has a precision of milliseconds – each index will be unique, then how effectively the index creation is done ? Any internals that will help me understand this ?

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Answers


  1. 0

    When you create an index on a TIMESTAMP field or any other data type, MySQL doesn’t store a hash of the value or the literal value itself as the index key. Instead, it uses a data structure called a B-tree (balanced tree) to organise and store index keys. B-trees are commonly used in databases because they provide efficient ways to search, insert, and delete data.

    What is an index key?

    An index key is a value or set of values extracted from one or more columns of a database table and used to build an index data structure. The purpose of an index key is to facilitate fast data retrieval and efficient querying of the table’s data. Each index key corresponds to a specific row in the table.

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  2. 0

    Indexes in databases are like bookmarks that help find rows with certain column values efficiently. Without an index, the database has to search through the entire table, starting from the beginning, to locate the needed rows. This becomes slower as the table gets bigger. But if the table has an index for the specific columns being searched, the database can quickly figure out where to jump to within the data, without scanning everything. This is way faster than reading each row one by one.

    Mainly there are two types of index mechanisms in MySQL. They are B-Tree Indexes and Hash Indexes.

    B-Tree Index Characteristics

    • Structure: B-tree (Balanced Tree) indexes are organized as a balanced tree structure where each node can have multiple keys and child pointers depending on the order (Maximum number of children a tree’s node could have) of the tree. This structure ensures that the tree remains balanced and optimized for range queries.
    • Key Distribution: B-trees are well-suited for cases where the data distribution is not uniform, and they handle a wide range of data values efficiently.
    • Support for Range Queries: B-tree indexes are particularly effective for range queries (e.g., SELECT WHERE column BETWEEN x AND y), as they allow for efficient traversal of data in a sorted order.
    • Insertion and Deletion: B-trees handle insertions and deletions efficiently while maintaining the balance of the tree. However, frequent insertions and deletions can lead to fragmentation over time.
    • Use Cases: B-tree indexes are commonly used for columns with high cardinality (many distinct values) and for queries involving range conditions.

    Hash Index Characteristics

    • Structure: Hash indexes use a hash function to map keys to specific locations (buckets) in the index. This allows for direct access to the desired data without the need for traversing a tree structure.
    • Key Distribution: Hash indexes work well when the data distribution is uniform and the hash function effectively distributes data across buckets.
    • Exact Match Queries: Hash indexes are optimized for exact match queries (e.g., SELECT WHERE column = x), as they provide constant-time access to the data if the hash function is well-distributed.
    • Insertion and Deletion: Hash indexes can be efficient for insertions and deletions when the hash function is well-designed and collisions (multiple keys mapping to the same bucket) are minimized. However, dealing with collisions can impact performance.
    • Use Cases: Hash indexes are often used for columns with low cardinality (few distinct values) and for scenarios where exact match queries are common.

    Comparison:

    • Query Types: B-tree indexes are versatile and support both range queries and exact match queries efficiently. Hash indexes excel at exact match queries but may not perform well with range queries.
    • Data Distribution: B-trees can handle non-uniform data distribution effectively, while hash indexes perform best when data distribution is uniform.
    • Insertion/Deletion: B-trees handle insertions and deletions well, especially for high cardinality columns. Hash indexes can perform well for insertions/deletions if hash collisions are minimized.
    • Memory Usage: B-trees generally consume more memory compared to hash indexes due to their tree structure.
    • Suitable Data Types: B-trees are suitable for a wide range of data types, while hash indexes are more suitable for integer or string columns with limited cardinality.

    Answer

    For a TimeStamp field, a B-tree index is generally a better choice than a hash index. B-tree indexes provide efficient support for both range queries and exact match queries. Since timestamps often involve queries for specific time ranges or ordered results, a B-tree index can provide better performance and more flexibility in these scenarios.

    When you create an index on a TimeStamp field in MySQL, it uses a B-tree indexing (default indexing mechanism) algorithm. This means that the actual timestamp values are stored in a sorted structure, not their hash.

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